Document

On the Difference of Effect on
Energy Consumption between
Energy Tax and Pre-Tax Energy Price:
An International Panel-Data Analysis on
Gasoline Demand
Park Seung-Joon
4. Nov. 2010
GCET 2010, Bangkok, Thailand
1
Question
• The meaning of Environmental Tax
Reform to address several environmental
problems.
• The Effect of Eco-Tax is related to
Price Elasticity of Demand
• Question: The Effect of Price Change and
Tax Hike is the same??
2
Volatile Oil Price
（WTI Forward $/bbl）
Source: http://chartpark.com/wti.html (Accessed on 2010.7.25)
3
Price Hike by...
• Market Development
“High price may be temporal, it may go
down soon suddenly.”
• Tax Hike (raising tax rate)
“Tax rate, once raised, cannot easily be
reduced.”
So, energy consumers may react
stronger, by changing behaviour, or
buying efficient car or appliances.
• How to Prove this presumption??
4
Existing Study
• Ghalwash (2007) “Signaling Effect of Tax”
Based on Household Expenditure Data and
using Almost Ideal Demand System Model,
revealed that Tax Elasticity is larger than Price
Elasticity.
• Bardazzi, Oropollo and Pazienza (2009)
Panel-Data Analysis of 5600 Italian Companies,
revealed that Tax Elasticity is larger than Price
Elasticity.
• But, what is “Tax Elasticity”?
5
Specification in Ghalwash(2007)
(Difficulty of Log-Linear)
Normal way to calculate price elasticity(β2)
(hard to separate p and t)
ln Y   0  1 ln X   2 ln( p  t )
Ghalwash defined the after-tax price (p) as the
product of pre-tax price ( p ) and tax factor (τ),
and log-linearized.
p  p   (That is,   p  p）
ln p  ln p  ln 
Then, we can separate the effects β2 and β3
ln Y   0  1 ln X   2 ln p  3 ln 
6
But this method makes difficulty
•(1) Market Price Development changes Tax Factor
Say, initial pre-tax price is 100[cent/litre], excise duty is 50
[cent/litre]. If pre-tax price goes up to 200[cent/litre], tax factor τ
goes down to 1.25 from initial 1.5. (Automatic Tax Cut??)
100
50 τ=1.5
200
50
τ=1.25
•(2) A Tax-Hike together with the same range of Price
Increase keeps the Tax Factor unchanged.
Say, initial pre-tax price is 50 [cent/litre], and specific duty rate is
also 50 [cent/litre]. If both of price and tax are raised by 10 [cent/litre],
tax factor remains unchanged. (Although tax has been raised!)
50
50
τ=2
60
60
τ=2
7
An Additional Specification
Simple Linear (not for elasticity)
• Simple Linear
pre-tax price
Excise Tax rate
Y   0  1 X   2 p   3t
We can find “difference” by this method.
But, how to prove the Significance?
Method to show the significance of
difference between β2 and β3
Y   0  1 X   2 p   2t   2t  3t
Y   0  1 X   2 ( p  t )  ( 3   2 )t
Gap: Significant??
8
Method and Data for Analysis
• Statistical Software STATA
• Panel Data Analysis on Gasoline in 29 OECD countries.
• IEA: Energy Prices & Taxes - Quarterly Statistics
IEA: Energy Balances of OECD Countries
Linear
gaspcit   i  1 gdppcit   2 ( prit  trit )  ( 3   2 )trit   it
gaspcit: Per Capita Gasoline Consumption [kg/pers.]
gdppcit: Per Capita Real GDP [1000$ppp/pers] vat
prit: Real Pre-Tax Gasoline Price [$ppp/Litre]
trit： Real Excise Tax Rate on Gasoline [$ppp/L] 1
μit： Error Term
Suffix “i” stands for country, “t” for year.
Deflator is U. S. CPI, base year 2000.
The VAT on pre-tax price is included in pre-tax price, and
the VAT on excise tax rate is included in the excise tax rate.
pre-tax
price
excise
tax rate
9
Linear and Log-Linear
Linear
gaspcit   i  1 gdppcit   2 ( prit  trit )  ( 3   2 )trit   it
Tax Rate
Log-Linear
ln gaspcit   i  1 ln gdppcit
  2 (ln prit  ln  it )  (  3   2 ) ln  it   it
Tax Factor
10
1000
0
500
gaspc
1500
[1] GDP and Gasoline Consumption
(Per Capita)
15
20
25
30
35
40
gdppc
Australia
Germany
Japan
United Kingdom
France
Italy
Korea
United States
11
1000
0
500
gaspc
1500
[2] After-Tax Price [$/litre] and
Per Capita Gasoline Consumption
0
.5
1
1.5
2
2.5
trpr
Australia
Germany
Japan
United Kingdom
France
Italy
Korea
United States
12
0
500
1000
gaspc
1500
[3] Tax Rate [$/litre] and
Per Capita Gasoline Consumption
0
.5
1
1.5
tr
Australia
Germany
Japan
United Kingdom
France
Italy
Korea
United States
13
Panel Data Analysis
• Test of Endogeneity (Kitamura 2007)
Log-Linear Specification
(Fixed Effect) significant (P=0.020)
(Random Effect) significant (P=0.060)
Hausman Statistics: -245.53
Linear Specification
(Fixed Effect) not significant (P=0.146)
(Random Effect) not significant (P=0.160)
Hausman Statistics: -6.99
14
Table 3 Estimated Results of log-linear specification Eq.(7)
lngdppc
(β1)
lnpr+lntfact
(β2)
lntfact
(β3-β2)
_cons
R2 within
R2 between
R2 overall
Number of...
Normal method
Fixed
Random
0.1567
0.2132
(0.000)***
(0.000)***
-0.5319
-0.5763
(0.000)***
(0.000)***
-0.0917
-0.1295
(0.000)***
(0.000)***
5.4560
5.2585
(0.000)***
(0.000)***
0.4264
0.4246
0.8722
0.8827
0.7829
0.7946
obs. 746, groups 29
IV method
Fixed
Random
0.0501
0.0880
(0.198)
(0.031)**
-0.6734
-0.7025
(0.000)***
(0.000)***
-0.1077
-0.1406
(0.000)***
(0.000)***
5.8297
5.6858
(0.000)***
(0.000)***
0.4253
0.4363
0.8637
0.8711
0.7926
0.7996
obs. 571, groups 24
-212.00
instrumented: lnpr+lntfact
instruments: lngdppc lntfact lnpcor
44.35 (p=0.000)***
IV
Hausman Stat.
Note: p-Value of t-test is in parentheses; level of significance, *** 1%, ** 5%, * 10%.
Breusch-Pagan test revealed significance at 1% level,
implying fixed effect panel is better than pooled OLS.
15
Table 3 Estimated Results of log-linear specification Eq.(7)
gdppc
(β1)
pr+tr
(β2)
tr
(β3-β2)
_cons
R2 within
R2 between
R2 overall
Number of...
IV
Hausman Stat.
Normal method
Fixed
Random
4.2307***
4.42427***
0.000
0.000
-65.93001*** -66.02336***
0.000
0.000
-10.5887
-16.4034
0.519
-0.325
415.5407*** 393.6381***
0.000
0.000
0.2360
0.2359
0.6361
0.6377
0.5177
0.5212
obs. 746, groups 29
-20.71
IV method
Fixed
Random
0.6641
0.7028
0.134
0.114
-94.8156***
-94.7810***
0.000
0.000
-22.5044
-25.3594
0.151
0.107
532.1231*** 512.2656***
0.000
0.000
0.3549
0.3559
0.6294
0.6305
0.5283
0.5306
obs. 571, groups 24
instrumented: trpr
instruments: gdppc tr pcor
-29.95
Note: p-Value of t-test is in parentheses; level of significance, *** 1%, ** 5%, * 10%.
Breusch-Pagan test revealed significance at 1% level,
implying fixed effect panel is better than pooled OLS.
16
Results
• The effect of Tax Rate is stronger than of Price
(Always significant by Log-Linear Sp.)
• By Log-Linear, the difference is +16.0%, and
Significant (p=0.000)
β3= -0.7811, β2= -0.6734; FE of IV Model
(In Line with Older Studies)
• By Linear, the difference is +27%, but barely
Insignificant (p=0.107)
β3= -1.201, β2= -0.948; RE of IV Model
17
Literature
Bardazzi, R, F. Oropallo and M. G. Pazienza (2009) “Industrial CO2
emissions in Italy: a microsimulation analysis of Environmental
Taxes on Firm’s Energy Demand” Critical Issues in
Environmental Taxation IV
Ghalwash, T. (2007) “Energy Taxes as a Signaling Device: An
Empirical Analysis of Consumer Preferences” Energy Policy, Vol.
35, Issue 1, p. 29-38
Schreiber, S. (2008) The Hausman Test Statistics can be Negative
even Asymptotically, Journal of Economics and Statistics
(Jahrbuecher fuer Nationaloekonomie und Statistik), 2008, vol.
228, issue 4, pages 394-405
Kitamura (2007) 北村行伸『パネルデータ分析』岩波書店
Tsutsui et al. (2008) 筒井淳也・平井裕久・秋吉美都・水落正明・坂本和
靖・福田亘孝(2007)『Stataで計量経済学入門』ミネルヴァ書房
18
Panel Data Analysis
• Test of Endogeneity (Kitamura 2007)
There is a doubt that prit+trit is endogenous (market price detre)
そこで、操作変数として、実質輸入原油費用pcorit[米$/bbl]を用いる。
prit  trit   i   1 gdppcit   2 trit   3 pcorit   it
を推定し、残差 ˆit
を計算する。式(7)に、 ˆit
を説明変数として含めることによって、
gaspct   i  1 gdppcit   2 ( prit  trit )  (  3   2 )trit   4ˆit   it
ˆit の係数(β4)が有意であれば、内生性があると見なされる。
検定の結果、内生性は無いと判定されたが、β4 のp値は
16%程度で、高くない（内生性が無いことを断定し難い）。
そのため、操作変数法も用いて結果を比較する。
19

Download Report